The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices.

The solidified state after the melting of the forming layer in the laser powder bed fusion (LPBF) directly reflects the final forming quality. Compared with the powder layer and the melt pool, it is easier to recognize and remove the defects of contour parts in time by monitoring and processing the solidified region after the melting of the forming layer. To explore the application of a solidified region image in defect contour detection of the forming layer, an improved image segmentation model based on TranUNet is designed to extract the image features of the solidified region as process data, on which basis this paper analyzes the similarities and differences between forming process data and CT scanning results data. Addressing the characteristics of large data volume and significant feature scale variation in the solidified region image obtained during the LPBF process, an SA-TransUNet semantic segmentation model integrating SE attention mechanism and ASPP multi-scale feature extraction module is developed to achieve high-precision solidified region image segmentation, with an $IoU$ and a dice coefficient index up to 94.24% and 97.02%, respectively. By extracting the solidified region image of the LPBF forming layer through this model and calculating the geometric feature values of its contour, a comparative analysis is conducted with the corresponding contour geometric feature values of the formed part CT scan image, which verifies the feasibility of the solidified region image extraction method proposed in this paper for contour defect detection.

Laser powder bed fusion (LPBF) technology is increasingly being applied to emergency rescue missions in high-altitude, island, and extremely cold environments. However, the complex atmospheric environment affects the quality consistency of LPBF-formed samples, which, to some extent, restricts the further development and application of LPBF technology in field manufacturing and emergency rescue. This study conducted on-site LPBF metal forming experiments in low-pressure, high-humidity, and extremely cold environments, exploring the impact of different atmospheric environments on the microstructure and mechanical properties of LPBF-formed 316L. The results show that different atmospheric environments, such as low pressure, high cold, and high humidity, have little effect on the density of LPBF 316L samples, with differences not exceeding 2.14%. The anisotropy differences in tensile properties in the X and Z directions of the deposited state are lower than those in the plain atmospheric environment. This paper provides a detailed explanation for the differences in tensile properties of X and Z direction samples and the abnormal reasons for the microstructure and properties of components formed in high humidity environments and proposes a series of improvement measures.